NSF-AoF: Collaborative Research: CIF: Small: 6G Wireless Communications via Enhanced Channel Modeling and Estimation, Channel Morphing and Machine Learning for mmWave Bands

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The project addresses challenges of next generation 6G wireless communication systems. For these systems, millimeter-wave (mmWave) and terahertz (THz) frequency bands that support wide bandwidth transmissions will play an important role in providing the advanced services envisioned of next generation systems. Due to the small wavelength, a key enabling technology for reliable and high data rate communication is the deployment of massive Multiple Input Multiple Output (MIMO) systems which consist of a very large number of antennas for transmission and reception. This allows for dense spatial sampling and use of spatial degrees of freedom for effective communication system design. However, the small form factor makes traditional radio-frequency (RF) circuitry design impractical due to circuit complexity, increased cost, and power consumption. These constraints lead to nonlinearities that call for developing nontraditional processing algorithms for which recently developed machine learning networks are suitable. Another challenge is the wireless channel which at these higher frequencies has significant path loss and varies in nature across different frequencies in the bands. To deal with the higher path loss there is a need for finding ways to enhance the quality of the channel, to which this project applies advanced channel morphing methods. The theoretical ideas resulting from the work will be supported with appropriate experimental work to lead to practically viable systems. The project will lead to state-of-the-art wireless communication systems that should help with maintaining leadership in wireless technology as well to train the next generation of researchers in this area of strategic importance.To develop next generation mmWave and THz based massive multiple input multiple-output (MIMO) wireless communication systems using machine learning (ML) algorithms, this project has four major components. One is ML-based sparse channel modeling in severely constrained environments, i.e., limited sensing, limited number of measurements, limited precision, and system imperfections. This work combines domain knowledge with data driven techniques to deal with the nonlinearities and imperfections in the system. A second component is novel channel modeling using block-sparse techniques and development of associated model-based and ML-based inference algorithms. Block channel structure is not analytically tractable in two dimensions and calls for ML techniques to learn from data. A third component is incorporation of reconfigurable intelligent surfaces (RISs) for channel morphing to improve channel quality. A final component of this project is experimental work, channel sounding and ray tracing, to support, validate, and refine the theoretical models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.